Shifting Paradigms in TNBC Treatment: Emerging Alternatives to Capecitabine in the Post-Neoadjuvant Setting
Bibliographic record
Abstract
BACKGROUND: Triple-negative breast cancer (TNBC) remains a clinically challenging subtype due to its aggressive nature and limited treatment options post-neoadjuvant failure. Historically, capecitabine has been the cornerstone of adjuvant therapy for TNBC patients not achieving a pathological complete response (pCR). However, the integration of new modalities such as immunotherapy and PARP inhibitors has prompted a re-evaluation of traditional post-neoadjuvant approaches. METHODS: This review synthesizes data from pivotal clinical trials and meta-analyses to evaluate the efficacy of emerging therapies in the post-neoadjuvant setting. We focus on the role of immune checkpoint inhibitors (ICIs), PARP inhibitors (PARPis), and antibody-drug conjugates (ADCs) alongside or in place of capecitabine in TNBC treatment paradigms. RESULTS: The addition of ICIs like pembrolizumab to neoadjuvant regimens has shown increased pCR rates and improved event-free survival, posing new questions about optimal post-neoadjuvant therapies. Similarly, PARPis have demonstrated efficacy in BRCA-mutated TNBC populations, with significant improvements in disease-free survival (DFS) and overall survival (OS). Emerging studies on ADCs further complicate the adjuvant landscape, offering potentially efficacious alternatives to capecitabine, especially in patients with residual disease after neoadjuvant therapy. DISCUSSION: The challenge remains to integrate these new treatments into clinical practice effectively, considering factors such as drug resistance, patient-specific characteristics, and socio-economic barriers. This review discusses the implications of these therapies and suggests a future direction focused on personalized medicine approaches in TNBC. CONCLUSIONS: As the treatment landscape for TNBC evolves, the role of capecitabine is being critically examined. While it remains a viable option for certain patient groups, the introduction of ICIs, PARPis, and ADCs offers promising alternatives that could redefine adjuvant therapy standards. Ongoing and future trials will be pivotal in determining the optimal therapeutic strategies for TNBC patients with residual disease post-neoadjuvant therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".